Techniques, methods & infrastructure

In regression modeling, we focus on penalized likelihood techniques for prediction and effect estimation. In several research projects, we have in particular investigated the Firth penalty (Jeffreys prior) as a solution to the problem of non-existence of regression coefficients estimated by maximum likelihood in various risk models. Other research has focused on algorithmic variable selection methods, on analyses of high-dimensional (omics) data or on the optimization of prediction models.

In evaluating new methodology, we use simulation studies, by which we can learn how methods perform under various conditions. In our simulation studies, we always try to define scenarios that are likely to be encountered in real-life data analysis. While in single-data set analyses the underlying population is usually unknown, simulation studies have the advantage that the population properties are defined by the experimentator, enabling the generalizability of results.